Ph.D. 2022
Joined Fall 2017Now: AI Scientist, Musco Sports Lighting, LLC
- Ph.D., Mechanical Engineering, Iowa State University, 2022
- B.S., Mechanical Engineering, Iowa State University, 2017
Sin Yong joined the lab in Fall 2017. He was involved in multiple projects, including the Autonomous Rover project and the Robot and Cop project, and gained hands-on experience with machine learning applications.
He graduated from the SCS Lab in December 2022 and is currently an AI Scientist at Musco Sports Lighting, LLC.
Research interests: Machine learning, deep learning, reinforcement learning, multimodal data, computer vision
Project: Autonomous Rover; Robot and Cop
Publications with the lab (19)
- Few-shot deep learning for AFM force curve characterization of single-molecule interactions — Patterns (2023)
- Active shooter detection and robust tracking utilizing supplemental synthetic data — arXiv preprint arXiv:2309.03381 (2023)
- Multimodal sensor fusion framework for residential building occupancy detection — Energy and buildings (2022)
Show all 19
- MDPGT: momentum-based decentralized policy gradient tracking — Proceedings of the AAAI conference on artificial intelligence (2022)
- Whisper: wireless home identification and sensing platform for energy reduction — Journal of Sensor and Actuator Networks (2021)
- A high-fidelity residential building occupancy detection dataset — Scientific Data (2021)
- Decentralized Deep Learning Using Momentum-Accelerated Consensus — Preprint (2021)
- Battery-free camera occupancy detection system — Proceedings of the 5th international workshop on embedded and mobile deep … (2021)
- Cross-gradient aggregation for decentralized learning from non-iid data — International conference on machine learning (2021)
- Spatiotemporal attention for multivariate time series prediction and interpretation — ICASSP 2021-2021 IEEE international conference on acoustics, speech and … (2021)
- Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems — Lecture notes in computer science (2020)
- Interpretable Deep Learning for Monitoring Combustion Instability — IFAC-PapersOnLine (2020)
- Granger causality based hierarchical time series clustering for state estimation — IFAC-PapersOnLine (2020)
- On higher-order moments in adam — Beyond First Order Methods in Machine Learning Workshop, NeurIPS 2019 (2019)
- An explainable framework using deep attention models for sequential data in combustion systems — NeurIPS 2019 workshop on machine learning and the physical sciences. NeurIPS (2019)
- For publication in 2019 acc a flexible framework for building occupancy detection using spatiotemporal pattern networks — 2019 American Control Conference (ACC) (2019)
- Learning State Switching for Improved Exploration in Multi-sensor Environments — (2019)
- Learning State Switching for Multi-sensor Integration — (2019)
- Temporal attention and stacked LSTMs for multivariate time series prediction — (2018)
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